Under no failures of assumptions, MAIC has produced unbiased treatment effect estimation in simulation studies [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}. Nevertheless, there are some concerns about its inefficiency and instability, particularly where covariate overlap is poor and effective sample sizes (ESSs) after weighting are small [9]}. These scenarios are pervasive in health technology appraisals [10]}. In these cases, weighting methods are sensitive to inordinate influence by a few subjects with extreme weights and are vulnerable to poor precision. A related concern is that feasible numerical solutions may not exist where there is no common covariate support [9]}, [12]}. Where overlap is weak, methods based on modeling the outcome expectation exhibit greater precision and efficiency than MAIC [9]}, [14]}, [15]}, [16]} but are prone to extrapolation, which may lead to severe bias under model misspecification [17]}, [18]}.
